Re-Work Young Researcher to Watch (2019)
Tensorflow Research Cloud Fellowship (2020)
Emergent Ventures Research Scholarship (Mercatus Centre) (2020)
Research Intern in University of Toronto’s Guzik Lab (2020)
Publications in Expert Opinions in Drug Discovery, NeurIPS Machine Learning for Molecules (2020-21)
Machine Learning Intern at Nurix Therapeutics (2021)
Computer Science student at UC Berkeley (2021)
Publication in European Laboratory for Learning and Intelligent Systems’ (ELLIS) Molecule Discovery Workshop (2021)
Machine Learning @ Autodesk (Spring 2022)
Machine Learning Research Intern at Dyno Therapeutics (Summer 2022)
My goal is to develop methods to understand biology using laboratory automation, high-throughput assays, and the combination of generative and probabilistic machine learning. By creating advanced tooling, I hope to solve important problems in biotech, healthcare and hard-tech.
I’m currently interning on the Machine Learning Research team at Dyno Therapeutics, a startup out of Harvard designing gene therapy vectors. There, I’m focusing on using deep learning methods to better understand sequence-function relationships for biological sequence design. I’m also a rising sophomore at UC Berkeley studying computer science and math.
Previously, I worked on developing cutting-edge applications for drug discovery using machine learning, as a research intern at Nurix Therapeutics and the Matter Lab (University of Toronto). Alongside my work there, I was on the open-source developer team for DeepChem, a project aiming to build scientific drug discovery tools using machine learning.